ploeh blog danish software design

Generics in .NET are wonderful, but sometimes when doing Test-Driven Development against a generic class I've felt frustrated because I've been feeling that dropping down to the lowest common denominator and testing against, say, Foo<object> doesn't properly capture the variability inherent in generics. On the other hand, writing the same test for five different types of T have seemed too wasteful (not to mention boring) to bother with.

That's until it occurred to me that in xUnit.net (and possibly other unit testing frameworks) I can define a generic test class. As an example, I wanted to test-drive a class with this class definition:

public class Interval<T>

Instead of writing a set of tests against Interval<object> I rather wanted to write a set of tests against a representative set of T. This is so easy to do that I don't know why I haven't thought of it before. I simply declared the test class itself as a generic class:

public abstract class IntervalFacts<T>

The reason I declared the class as abstract is because that effectively prevents the test runner from trying to run the test methods directly on the class. That would fail because T is still open. However, it enabled me to write tests like this:

[Theory, AutoCatalogData]
public void MinimumIsCorrect(IComparable<T> first, 
    IComparable<T> second)
{
    var sut = new Interval<T>(first, second);
    IComparable<T> result = sut.Minimum;
    Assert.Equal(result, first);
}

In this test, I also use AutoFixture's xUnit.net extension, but that's completely optional. You might as well just write an old-fashioned unit test, but then you'll need a SUT Factory that can resolve generics. If you don't use AutoFixture any other (auto-mocking) container will do, and if you don't use one of those, you can define a Factory Method that creates appropriate instances of T (and, in this particular case, instances of IComparable<T>).

In the above example I used a [Theory], but I might as well have been using a [Fact] as long as I had a container or a Factory Method to create the appropriate instances.

The above test doesn't execute in itself because the owning class is abstract. I needed to declare an appropriate set of constructed types for which I wanted the test to run. To do that, I defined the following test classes:

public class DecimalIntervalFacts : IntervalFacts<decimal> { }
public class StringIntervalFacts : IntervalFacts<string> { }
public class DateTimeIntervalFacts : IntervalFacts<DateTime> { }
public class TimSpanIntervalFacts : IntervalFacts<TimeSpan> { }

The only thing these classes do is to each pick a particular type for T. However, since they are concrete classes with test methods, the test runner will pick up the test cases and execute them. This means that the single unit test I wrote above is now being executed four times - one for each constructed type.

It's even possible to specialize the generic class and add more methods to a single specialized class. As a sanity check I wanted to write a set of tests specifically against Interval<int>, so I added this class as well:

public class Int32IntervalFacts : IntervalFacts<int>
{
    [Theory]
    [InlineData(0, 0, 0, true)]
    [InlineData(-1, 1, -1, true)]
    [InlineData(-1, 1, 0, true)]
    [InlineData(-1, 1, 1, true)]
    [InlineData(-1, 1, -2, false)]
    [InlineData(-1, 1, 2, false)]
    public void Int32ContainsReturnsCorrectResult(
        int minimum, int maximum, int value,
        bool expectedResult)
    {
        var sut = new Interval<int>(minimum, maximum);
        var result = sut.Contains(value);
        Assert.Equal(expectedResult, result);
    }
 
    // More tests...
}

When added as an extra class in addition to the four ‘empty' concrete classes above, it now causes each generic method to be executed five times, whereas the above unit test is only executed for the Int32IntervalFacts class (on the other hand it's a parameterized test, so the method is actually executed six times).

It's also possible to write parameterized tests in the generic test class itself:

[Theory]
[InlineData(-1, -1, false)]
[InlineData(-1, 0, true)]
[InlineData(-1, 1, true)]
[InlineData(0, -1, false)]
[InlineData(0, 0, true)]
[InlineData(0, 1, true)]
[InlineData(1, -1, false)]
[InlineData(1, 0, false)]
[InlineData(1, 1, false)]
public void ContainsReturnsCorrectResult(
    int minumResult, int maximumResult,
    bool expectedResult)
{
    // Test method body
}

Since this parameterized test in itself has 9 variations and is declared by IntervalFacts<T> which now has 5 constructed implementers, this single test method will be executed 9 x 5 = 45 times!

Not that the number of executed tests in itself is any measure of the quality of the test, but I do appreciate the ability to write generic unit tests against generic types.

Since I announced AutoFixture 2.1 beta 1 no issues have been reported, so I've now promoted the release to the official 2.1 release. This means that if you already downloaded AutoFixture 2.1 beta 1, you already have the 2.1 binaries. If not, you can head over to the release page to get it.

The NuGet packages will soon be available.

Update (2011.5.4): The NuGet packages are now available.

Recently I partook in a Windows Azure migration workshop, helping developers from existing development organizations port their applications to Windows Azure. Once more an old design smell popped up: SQL Server over-utilization. This ought to be old news to anyone with experience designing software on the Wintel stack, but apparently it bears repetition:

Don't put logic in your database. SQL Server should be used only for persistent storage of data.

(Yes: this post is written in 2011…)

Many years ago I heard that role described as a ‘bit bucket' - you put in data and pull it out again, and that's all you do. No fancy stored procedures or functions or triggers.

Why wouldn't we want to use the database if we have one? Scalability is the answer. SQL Server doesn't scale horizontally. You can't add more servers to take the load off a database server (well, some of my old colleagues will argue that this is possible with Oracle, and that may be true, but with SQL Server it's impossible).

Yes, we can jump through hoops like partitioning and splitting the database up into several smaller databases, but it still doesn't give us horizontal scalability. SQL Server is a bottleneck in any system in which it takes part.

How is this relevant to Windows Azure? It's relevant for two important reasons:

• There's an upper size limit on SQL Azure. Currently that size limit is 50 GB, and while it's likely to grow in the future, there's going to be a ceiling for a long time.
• You can't fine tune the hardware for performance. The server runs on virtual hardware.

Development organizations that rely heavily on the database for execution of logic often need expensive hardware and experienced DBAs to squeeze extra performance out of the database servers. Such people know that write-intensive/append-only tables work best with one type of RAID, while read-intensive tables are better hosted on other file groups on different disks with different RAID configurations.

With SQL Azure you can just forget about all that.

The bottom line is that there are fundamental rules for software development that you must follow if you want to be able to successfully migrate to Windows Azure. I previously described an even simpler sanity check you should perform, but after that you should take a good look at your database.

The best solution is if you can completely replace SQL Server with Azure's very scalable storage services, but those come with their own set of challenges.

Rapid feedback is one of the cornerstones of agile development. The faster we can get feedback, the less it costs to correct errors. Unit testing is one of the ways to get feedback, but not the only one. Each way we can get feedback comes with its own advantages and disadvantages.

In my experience there's a tradeoff between the cost of setting up a feedback mechanism and the level of confidence we can get from it. This is quite intuitive to most people, but I've rarely seen it explicitly described. The purpose of this post is to explicitly describe and relate those different mechanisms.

Compilation #

In compiled languages, compilation provides the first level of feedback. I think a lot of people don't think about this as feedback as (for compiled languages) it's a necessary step before the code can be executed.

The level of confidence may not be very high - we tend to laugh at statements like ‘if it compiles, it works'. However, the compiler still catches a lot of mistakes. Think about it: does your code always compile, or do you sometimes get compilation errors? Do you sometimes try to compile your code just to see if it's possible? I certainly do, and that's because the compiler is always available, and it's the first and fastest level of feedback we get.

Code can be designed to take advantage of this verification step. Constructor Injection, for example, ensures that we can't create a new instance of a class without supplying it with its required dependencies.

It's also interesting to note that anecdotal evidence seems to suggest that unit testing is more prevalent for interpreted languages. That makes a lot of sense, because as developers we need feedback as soon as possible, and if there's no compiler we must reach for the next level of feedback mechanism.

Static code analysis #

The idea of static code analysis isn't specific to .NET, but in certain versions of Visual Studio we have Code Analysis (which is also available as FxCop). Static code analysis is a step up from compilation - not only is code checked for syntactical correctness, but it's also checked against a set of known anti-patterns and idioms.

Static code analysis is encapsulated expert knowledge, yet surprisingly few people use it. I think part of the reason for this is because the ratio of time against confidence isn't as compelling as with other feedback mechanism. It takes some time to run the analysis, but like compilation the level of confidence gained from getting a clean result is not very high.

Personally, I use static code analysis once in a while (e.g. before checking in code to the default branch), but not as often as other feedback mechanisms. Still, I think this type of feedback mechanism is under-utilized.

Unit testing #

Running a unit test suite is one of the most efficient ways to gain rapid feedback. The execution time is often comparable to running static code analysis while the level of confidence is much higher.

Obviously a successful unit test execution is no guarantee that the final application works as intended, but it's a much better indicator than the two previous mechanisms. When presented with the choice of using either static code analysis or unit tests, I prefer unit tests every time because the confidence level is much higher.

If you have sufficiently high code coverage I'd say that a successful unit test suite is enough confidence to check in code and let continuous integration take care of the rest.

Keep in mind that continuous integration and other types of automated builds are feedback mechanisms. If you institute rules where people are ‘punished' for checking in code that breaks the build, you effectively disable the automated build as a source of feedback.

Thus, the rest of these feedback mechanisms should be used rarely (if at all) on developer work stations.

Integration testing #

Integration testing comes in several sub-categories. You can integrate multiple classes from within the same library, or integrate multiple libraries while still using Test Doubles instead of out-of-process resources. At this level, the cost/confidence ratio is comparable to unit testing.

Subcutaneous testing #

A more full level of integration testing comes when all resources are integrated, but the tests are still driven by code instead of through the UI. While this gives a degree of confidence that is close to what you can get from a full systems test, the cost of setting up the entire testing environment is much higher. In many cases I consider this the realm of a dedicated testing department, as it's often a full-time job to maintain the suite of automation tools that enable such tests - particularly if we are talking about distributed systems.

System testing #

This is a test of the full system, often driven through the UI and supplemented with manual testing (such as exploratory testing). This provides the highest degree of confidence, but comes with a very high cost. It's often at this level we find formal acceptance tests.

The time before we can get feedback at this level is often considerable. It may take days or even weeks or months.

Understand the cost/confidence ratio #

The purpose of this post has been to talk about different ways we can get feedback when developing software. If we could get instantaneous feedback with guaranteed confidence it would be easy to choose, but as this is not the case we must understand the benefits and drawbacks of each mechanism.

It's important to realize that there are many mechanisms, and that we can combine them in a staggered approach where we start with fast feedback (like the compiler) with a low degree of confidence and then move through successive stages that each provide a higher level of confidence.

Comments

Developers exposed to ASP.NET are likely to be familiar with the so-called Provider pattern. You see it a lot in that part of the BCL: Role Provider, Membership Provider, Profile Provider, etc. Lots of text has already been written about Providers, but the reason I want to add yet another blog post on the topic is because once in a while I get the question on how it relates to Dependency Injection (DI).

Is Provider a proper way to do DI?

No, it has nothing to do with DI, but as it tries to mimic loose coupling I can understand the confusion.

First things first. Let's start with the name. Is it a pattern at all? Regular readers of this blog may get the impression that I'm fond of calling everything and the kitchen sink an anti-pattern. That's not true because I only make that claim when I'm certain I can hold that position, so I'm not going to denounce Provider as an anti-pattern. On the contrary I will make the claim that Provider is not a pattern at all.

A design pattern is not invented - it's discovered as a repeated solution to a commonly recurring problem. Providers, on the other hand, were invented by Microsoft, and I've rarely seen them used outside their original scope. Secondly I'd also dispute that they solve anything.

That aside, however, I want to explain why Provider is bad design:

• It uses the Constrained Construction anti-pattern
• It hides complexity
• It prevents proper lifetime management
• It's not testable

In the rest of this post I will explain each point in detail, but before I do that we need an example to look at. The old OrderProcessor example suffices, but instead of injecting IOrderValidator, IOrderCollector, and IOrderShipper this variation uses Providers to provide instances of the Services:

public SuccessResult Process(Order order)
{
    IOrderValidator validator = 
        ValidatorProvider.Validator;
    bool isValid = validator.Validate(order);
    if (isValid)
    {
        CollectorProvider.Collector.Collect(order);
        ShipperProvider.Shipper.Ship(order);
    }
 
    return this.CreateStatus(isValid);
}

The ValidatorProvider uses the configuration system to create and return an instance of IOrderValidator:

public static IOrderValidator Validator
{
    get 
    {
        var section = 
            OrderValidationConfigurationSection
                .GetSection();
        var typeName = section.ValidatorTypeName;
        var type = Type.GetType(typeName, true);
        var obj = Activator.CreateInstance(type);
        return (IOrderValidator)obj;
    }
}

There are lots of details I omitted here. I could have saved the reference for later use instead of creating a new instance each time the property is accessed. In that case I would also have had to make the code thread-safe, so I decided to skip that complexity. The code could also be more defensive, but I'm sure you get the picture.

The type name is defined in the app.config file like this:

<orderValidation 
  type="Ploeh.Samples.OrderModel.UnitTest.TrueOrderValidator,
        Ploeh.Samples.OrderModel.UnitTest" />

Obviously, CollectorProvider and ShipperProvider follow the same… blueprint.

This should be well-known to most .NET developers, so what's wrong with this model?

Constrained Construction #

In my book's chapter on DI anti-patterns I describe the Constrained Construction anti-pattern. Basically it occurs every time there's an implicit constraint on the constructor of an implementer. In the case of Providers the constraint is that each implementer must have a default constructor. In the example the culprit is this line of code:

var obj = Activator.CreateInstance(type);

This constrains any implementation of IOrderValidator to have a default constructor, which obviously means that the most fundamental DI pattern Constructor Injection is out of the question.

Variations of the Provider idiom is to supply an Initialize method with a context, but this creates a temporal coupling while still not enabling us to inject arbitrary Services into our implementations. I'm not going to repeat six pages of detailed description of Constrained Construction here, but the bottom line is that you can't fix it - you have to refactor towards true DI - preferably Constructor Injection.

Hidden complexity #

Providers hide the complexity of their implementations. This is not the same as encapsulation. Rather it's a dishonest API and the problem is that it just postpones the moment when you discover how complex the implementation really is.

When you implement a client and use code like the following everything looks deceptively simple:

IOrderValidator validator = 
    ValidatorProvider.Validator;

However, if this is the only line of code you write it will fail, but you will not notice until run-time. Check back to the implementation of the Validator property if you need to refresh the implementation: there's a lot of things that can go wrong here:

• The appropriate configuration section is not available in the app.config file.
• The ValidatorTypeName is not provided, or is null, or is malformed.
• The ValidatorTypeName is correctly formed, but the type in question cannot be located by Fusion.
• The Type doesn't have a default constructor. This is one of the other problems of Constrained Construction: it can't be statically enforced because a constructor is not part of an abstraction's API.
• The created type doesn't implement IOrderValidator.

I'm sure I even forgot a thing or two, but the above list is sufficient for me. None of these problems are caught by the compiler, so you don't discover these issues until you run an integration test. So much for rapid feedback.

I don't like APIs that lie about their complexity.

Hiding complexity does not make an API easier to use; it makes it harder.

An API that hides necessary complexity makes it impossible to discover problems at compile time. It simply creates more friction.

Lifetime management issues #

A Provider exerts too much control over the instances it creates. This is a variation of the Control Freak anti-pattern (also from my book). In the current implementation the Validator property totally violates the Principle of least surprise since it returns a new instance every time you invoke the getter. I did this to keep the implementation simple (this is, after all, example code), but a more normal implementation would reuse the same instance every time.

However, reusing the same instance every time may be problematic in a multi-threaded context (such as a web application) because you'll need to make sure that the implementation is thread-safe. Often, we'd much prefer to scope the lifetime of the Service to each HTTP request.

HTTP request scoping can be built into the Provider, but then it would only work in web applications. That's not very flexible.

What's even more problematic is that once we move away from the Singleton lifestyle (not to be confused with the Singleton design pattern) we may have a memory leak at hand, since the implementation may implement IDisposable. This can be solved by adding a Release method to each Provider, but now we are moving so far into DI Container territory that I find it far more reasonable to just use proper DI instead of trying to reinvent the wheel.

Furthermore, the fact that each Provider owns the lifetime of the Service it controls makes it impossible to share resources. What if the implementation we want to use implements several Role Interfaces each served up by a different Provider? We might want to use that common implementation to share or coordinate state across different Services, but that's not possible because we can't share an instance across multiple providers.

Even if we configure all Providers with the same concrete class, each will instantiate and serve its own separate instance.

Testability #

The Control Freak also impacts testability. Since a Provider creates instances of interfaces based on XML configuration and Activator.CreateInstance, there's no way to inject a dynamic mock.

It is possible to use hard-coded Test Doubles such as Stubs or Fakes because we can configure the XML with their type names, but even a Spy is problematic because we'll rarely have an object reference to the Test Double.

In short, the Provider idiom is not a good approach to loose coupling. Although Microsoft uses it in some of their products, it only leads to problems, so there's no reason to mimic it. Instead, use Constructor Injection to create loosely coupled components and wire them in the application's Composition Root using the Register Resolve Release pattern.

Comments

Implementor < Service < Provider (or ServiceProvider)

Model < StorageService < MySQLProvider

It gives me great pleasure to announce that AutoFixture 2.1 beta 1 is now available for download. Compared to version 2.0 this release mostly contains added features as well as a few bug fixes. The release page contains a list of the new features.

The general availability of beta 1 of AutoFixture 2.1 marks the beginning of a trial period. If no new issues are reported within the next few weeks, a final version 2.1 will be released. If too many issues are reported, a new beta version may be necessary.

Please report any issues you find.

NuGet packages will follow the RTW version.

When AutoFixture creates complex objects it invokes the constructors of the types in question. When a class exposes more than one constructor, the default behavior is to pick the constructor with the fewest number  of arguments. This is the exact opposite of most DI Containers that select the greediest constructor.

For a DI Container it makes more sense to select the greediest constructor because that maximizes the chance that all dependencies are properly being auto-wired.

The reason that AutoFixture's default behavior is different is that it maximizes the chance that an object graph can be created at all. If a convenience overload is available, this gives AutoFixture a better chance to compose the object graph.

This works well until a class comes along and introduces the wrong kind of ambiguity. Consider this case of Bastard Injection (Dependency Injection in .NET, section 5.2):

public class Bastard
{
    private readonly IFoo foo;
 
    public Bastard()
        : this(new DefaultFoo())
    {
    }
 
    public Bastard(IFoo foo)
    {
        if (foo == null)
        {
            throw new ArgumentNullException("foo");
        }
 
        this.foo = foo;
    }
 
    public IFoo Foo
    {
        get { return this.foo; }
    }
}

By default AutoFixture will use the default constructor which means that the Foo will always be the instance of DefaultFoo owned by the Bastard class itself. This is problematic if we wish to inject and freeze a Test Double because even if we freeze an IFoo instance, it will never be injected because the default constructor is being invoked.

var fixture = new Fixture();
fixture.Register<IFoo>(
    fixture.CreateAnonymous<DummyFoo>);
var b = fixture.CreateAnonymous<Bastard>();
Assert.IsAssignableFrom<DefaultFoo>(b.Foo);

As the above unit test demonstrates, even though the Register method call defines a mapping from IFoo to DummyFoo, the Foo property is an instance of DefaultFoo. The DummyFoo instance is never injected into the Bastard instance since the default constructor is used.

Your first reaction in such a case should be to get rid of all convenience constructors to make the class' constructor unambiguous. However, it's also possible to change AutoFixture's behavior.

The following is a description of a feature that will be a available in AutoFixture 2.1. It's not available in AutoFixture 2.0, but is already available in the code repository. Thus, if you can't wait for AutoFixture 2.1 you can download the source and build it.

As always, AutoFixture's very extensible interface makes it possible to change this behavior. Constructor selection is guided by an interface called IConstructorQuery, and while ModestConstructorQuery is the default implementation, there's also an implementation called GreedyConstructorQuery.

To change the behavior specifically for the Bastard class the Fixture instance must be customized. The following unit test demonstrates how to do that.

var fixture = new Fixture();
fixture.Customize<Bastard>(c => c.FromFactory(
    new MethodInvoker(
        new GreedyConstructorQuery())));
fixture.Register<IFoo>(
    fixture.CreateAnonymous<DummyFoo>);
var b = fixture.CreateAnonymous<Bastard>();
Assert.IsAssignableFrom<DummyFoo>(b.Foo);

Notice that the only difference from before is an additional call to the Customize method where Bastard is modified to use greedy constructor selection. This changes the factory for the Bastard type, but not for any other types.

If you'd rather prefer to completely change the overall constructor selection behavior for AutoFixture you can add the GreedyConstructorQuery wrapped in a MethodInvoker to the Fixture's Customizations:

var fixture = new Fixture();
fixture.Customizations.Add(
    new MethodInvoker(
        new GreedyConstructorQuery()));
fixture.Register<IFoo>(
    fixture.CreateAnonymous<DummyFoo>);
var b = fixture.CreateAnonymous<Bastard>();
Assert.IsAssignableFrom<DummyFoo>(b.Foo);

In this unit test, the only change from the previous is that instead of assigning the GreedyConstructorQuery specifically to Bastard, it's now assigned as the new default strategy. All specimens created by AutoFixture will be created by invoking their most greedy constructor.

As always I find the default behavior more attractive, but the option to change behavior is there if you need it.

Comments

namespace JustATest
{
	using Ploeh.AutoFixture;
	using Ploeh.AutoFixture.Kernel;
 
	using Xunit;
 
	public class JustATest {
 
		[Fact]
		public void GreedyConstructorAndOmitAutoProps() {
			var fixture = new Fixture();
			fixture.Customize<Foo>(c => c.FromFactory(
				new MethodInvoker(
					new GreedyConstructorQuery())));
			fixture.Customize<Foo>(c => c.OmitAutoProperties());
 
			var foo = fixture.Create<Foo>();
			Assert.NotNull(foo.myStr);
		}
	}
 
	public class Foo {
		public Foo(string myStr) {
			this.myStr = myStr;
		}
 
		public Foo() {
		}
 
		public string myStr { get; set; }
	}
}

I just got a question about AutoFixture's way of dealing with enumerables, and since I suspect this is something that might surprise a few people I found it reasonable to answer in a blog post.

In short, this unit test succeeds:

var fixture = new Fixture();
var expected = fixture.CreateMany<string>();
Assert.False(expected.SequenceEqual(expected));

If this doesn't surprise you, then read the assertion again. The sequence is expected to not equal itself!

This behavior is by design.

(I've always wanted to write that.) The reason is that the return type of the CreateMany method is IEnumerable<T>, and that interface makes no guarantee that it will return the same sequence every time you iterate over it. The implementation may be a Generator.

According to its principle of Constrained Non-determinism, AutoFixture goes to great lengths to ensure that since IEnumerable<T> might be non-deterministic, it will be. This can be very useful in flushing out incorrect assumptions made by the SUT.

This behavior is carried forward by the MultipleCustomization. This unit test also succeeds:

var fixture = new Fixture()
    .Customize(new MultipleCustomization());
var expected =
    fixture.CreateAnonymous<IEnumerable<string>>();
Assert.False(expected.SequenceEqual(expected));

The behavior is the same because with the MultipleCustomization IEnumerable<T> acts as a relationship type. The class responsible for this behavior is called FiniteSequenceRelay, but AutoFixture 2.1 will also ship with an alternative StableFiniteSequenceRelay which wraps the sequence in a List<T>.

To change the default behavior you can add the StableFiniteSequenceRelay to a Fixture's customizations:

var fixture = new Fixture();
var stableRelay = new StableFiniteSequenceRelay();
fixture.Customizations.Add(stableRelay);
 
var expected =
    fixture.CreateMany<string>();
Assert.True(expected.SequenceEqual(expected));

The above unit test succeeds. Notice that the assertion now verifies that the sequence of strings is equal to itself.

Just like MultipleCustomization the StableFiniteSequenceRelay class will be available in AutoFixture 2.1, but is not availale in 2.0.

As always, it's best to encapsulate this behavior change into a Customization:

public class StableFiniteSequenceCustomization :
    ICustomization
{
    public void Customize(IFixture fixture)
    {
        var stableRelay = 
            new StableFiniteSequenceRelay();
        fixture.Customizations.Add(stableRelay);
    }
}

This makes it easier to bundle it with the MultipleCustomization if we want all the other benefits of conventions for multiples:

public class StableMultipeCustomization : 
    CompositeCustomization
{
    public StableMultipeCustomization()
        : base(
            new StableFiniteSequenceCustomization(),
            new MultipleCustomization())
    {
    }
}

With the StableMultipleCustomization the following unit test now passes:

var fixture = new Fixture()
    .Customize(new StableMultipeCustomization());
var expected =
    fixture.CreateAnonymous<IEnumerable<string>>();
Assert.True(expected.SequenceEqual(expected));

I still prefer the default behavior for its potential to act as an early warning system, but as I realize that this behavior may be problematic in some scenarios, the StableFiniteSequenceRelay provides an easy way to change that behavior.

Update (2011.8.10): StableFiniteSequenceCustomization is now part of AutoFixture and will be available in AutoFixture 2.2.

Comments

My latest MSDN Magazine article, this time about CQRS on Windows Azure, is now available at the April MSDN Magazine web site.

It's mostly meant as an introduction to CQRS as well as containing some tips and tricks that are specific to applying CQRS on Windows Azure.

As an added bonus the code sample download contains lots of idiomatic unit tests written with AutoFixture's xUnit.net extensions, so if you'd like to see the result of my TDD work with AutoFixture, there's a complete code base to look at there.

Comments

A few months back I wrote a (somewhat theoretical) post on composable interfaces. A major point of that post was that Role Interfaces  with a single Command method (i.e. a method that returns no value) is a very versatile category of abstraction.

Some of my readers asked for examples, so in this post I will provide a few. Consider this interface that fits the above description:

public interface IMessageConsumer<T>
{
    void Consume(T message);
}

This is a very common type of interface you will tend to encounter a lot in distributed, message-based architectures such as CQRS or Udi Dahan's view of SOA. Some people would call it a message subscriber instead...

In the rest of this post I will examine how we can create compositions out of the IMessageConsumer<T> interface using (in order of significance) Decorator, Null Object, Composite, and other well-known programming constructs.

Decorator #

Can we create a meaningful Decorator around the IMessageConsumer<T> interface? Yes, that's easy - I've earlier provided various detailed examples of Decorators, so I'm not going to repeat them here.

I've yet to come up with an example of an interface that prevents us from applying a Decorator, so it's a valid falsifiable claim that we can always Decorate an interface. However, I have yet to prove that this is true, so until now we'll have to call it a conjecture.

However, since it's so easy to apply a Decorator to an interface, it's not a particularly valuable trait when evaluating the composability of an interface.

Null Object #

It can be difficult to implement the Null Object pattern when the method(s) in question return a value, but for Commands it's easy:

public class NullMessageConsumer<T> : IMessageConsumer<T>
{
    public void Consume(T message)
    {
    }
}

The implementation simply ignores the input and does nothing.

Once again my lack of formal CS education prevents me from putting forth a formal proof, but I strongly suspect that it's always possibly to apply the Null Object pattern to a Command (keep in mind that out parameters count as output, so any interface with one or more of these are not Commands).

It's often valuable to be able to use a Null Object, but the real benefit comes when we can compose various implementations together.

Composite #

To be truly composable, an interface should make it possible to create various concrete implementations that each adhere to the Single Responsibility Principle and then compose those together in a complex implementation. A Composite is a general-purpose implementation of this concept, and it's easy to create a Composite out of a Command:

public class CompositeMessageConsumer<T> : 
    IMessageConsumer<T>
{
    private readonly IEnumerable<IMessageConsumer<T>> 
        consumers;
 
    public CompositeMessageConsumer(
        params IMessageConsumer<T>[] consumers)
    {
        if (consumers == null)
        {
            throw new ArgumentNullException("consumers");
        }
 
        this.consumers = consumers;
    }
 
    public IEnumerable<IMessageConsumer<T>> Consumers
    {
        get { return this.consumers; }
    }
 
    #region IMessageConsumer<T> Members
 
    public void Consume(T message)
    {
        foreach (var consumer in this.Consumers)
        {
            consumer.Consume(message);
        }
    }
 
    #endregion
}

The implementation of the Consume method simply loops over each composed IMessageConsumer<T> and invokes its Consume method.

I can, for example, implement a sequence of actions that will take place by composing the individual concrete implementations. First we have a guard that protects against invalid messages, followed by a consumer that writes the message to a persistent store, completed by a consumer that raises a Domain Event that the reservation request was accepted.

var c = new CompositeMessageConsumer<MakeReservationCommand>(
    guard, 
    writer, 
    acceptRaiser);

Consumers following the Liskov Substitution Principle will not notice the difference, as all they will see is an implementation of IMessageConsumer<MakeReservationCommand>.

More advanced programming constructs #

The Composite pattern only describes a single, general way to compose implementations, but with a Command interface we can do more. As Domain-Driven Design explains, a successful interface is often characterized by making it possible to apply well-known arithmetic or logical operators. As an example, in the case of the IMessageConsumer<T> interface, we can easily mimic the well-known ?! ternary operator from C#:

public class ConditionalMessageConsumer<T> : 
    IMessageConsumer<T>
{
    private Func<T, bool> condition;
    private IMessageConsumer<T> first;
    private IMessageConsumer<T> second;
 
    public ConditionalMessageConsumer(
        Func<T, bool> condition, 
        IMessageConsumer<T> first, 
        IMessageConsumer<T> second)
    {
        if (condition == null)
        {
            throw new ArgumentNullException("condition");
        }
        if (first == null)
        {
            throw new ArgumentNullException("first");
        }
        if (second == null)
        {
            throw new ArgumentNullException("second");
        }
 
        this.condition = condition;
        this.first = first;
        this.second = second;
    }
 
    public void Consume(T message)
    {
        (this.condition(message) ? this.first : this.second)
            .Consume(message);
    }
}

This is more verbose than the ?! operator because C# doesn't allow us to define operator overloads for interfaces, but apart from that, it does exactly the same thing. Notice particularly that the Consume method uses the ?! operator to select among the two alternatives, and then subsequently invokes the Consume method on the selected consumer.

We can use the ConditionalMessageConsumer to define branches in the consumption of messages. As an example, we can encapsulate the previous CompositeMessageConsumer<MakeReservationCommand> into a conditional branch like this:

var consumer = 
    new ConditionalMessageConsumer<MakeReservationCommand>(
        guard.HasCapacity, c, rejectRaiser);

Notice that I use the method group syntax to supply the condition delegate. If the HasCapacity method returns true, the previous composite (c) is being invoked, but if the result is false we instead use a consumer that raises the Domain Event that the reservation request was rejected.

Concluding thoughts #

Apart from the direct purpose of providing examples of the immensely powerful composition options a Command interface provides I want to point out a couple of things:

• Each of the design pattern implementations (Null Object, Composite - even Conditional) are generic. This is a strong testament to the power of this particular abstraction.
• The dynamic mocks I'm familiar with (Moq, Rhino Mocks) will by default try to create Null Object implementations for interfaces without explicit setups. Since it's trivial to implement a Null Command, they just emit them by default. If you use an Auto-mocking Container with your unit tests, you can refactor to your heart's content, adding and removing dependencies as long as they are Command interfaces, and your testing infrastructure will just take care of things for you. It'll just work.
• Did you notice that even with these few building blocks we have implemented a large part of a sequential workflow engine? We can execute consumers in sequence as well as branch between different sequences. Obviously, more building blocks are needed to make a full-blown workflow engine, but not that many. I'll leave the rest as an exercise to the reader :)

As I originally sketched, a Command interface is the ultimate in composability. To illustrate, the application from where I took the above examples is a small application with 48 types (classes and interfaces) in the production code base (that is, excluding unit tests). Of these, 9 are implementations of the IMessageConsumer<T> interface. If we also count the interface itself, it accounts for more than 20 percent of the code base. According to the Reused Abstractions Principle (RAP) I consider this a very successful abstraction.